One of the most damaging effects of normal aging is an increasing difficulty in recalling recent events especially when those events are subject to interference from related memories learned in different contexts. Although this difficulty in forming and recalling episodic memories is a hallmark of the aging process, the mechanisms that underlie it remain largely unknown. We adopt a computational modeling approach to help elucidate these mechanisms in three major laboratory memory tasks and in more naturalistic real world tasks. The key innovation of this application is in its proposal to fit high-resolutio individual subject data obtained in multisession experiments and to examine age-differences in terms of the distributions of best fitting parameters, which will allow us to detect subgroups of older adults that show different profiles of impairment. Given data of sufficiently high resolution this novel approach to aging research has become feasible thanks to advances in high-performance computing. Computational modeling of age-affected memory systems could lead not only to a better understanding of aging memory, but also provide a theoretical basis on which to build potential schemes for memory remediation among older adults suffering age-related memory impairment.